These tasks could include context understanding or anything else that takes a human more than 1s to do.

AI is very good at executing narrowly defined tasks and fares less well in uncertain environments.

Understanding the scope and boundaries of AI is paramount before planning your AI efforts.

Leverage AI to create an advantage specific to your industry sector.

— Andrew NgNow that you understand how the AI Strategy relates to the business strategy and which opportunities and challenges exist, how do you approach creating one?The Core Components of an AI StrategyIn the same way that every company was touched by electricity, every company will be touched by the power of AI.

While no AI Strategy looks the same, all AI Strategies need to answer similar questions.

The core components of any AI Strategy concern its holy trinity of Data, Infrastructure, Algorithms, surrounded by the pillars of Skills and Organization.

Let’s dive into each component.

DataWithout data, there can be no AI.

Data relates to all pieces of information that are relevant to improve your business.

It can be anything from sensor data of self-driving cars to financial data for business decisions.

Creating a Data Strategy is a vital part of any AI Strategy.

Andrew Ng recommends touching on the following points:What data can you acquire strategically?Do you collect all or selected data?Jason Risch, AI Investor at the AI Fund, stresses the importance of the right timing of strategic data acquisition.

Vast amounts of resources are also available to help you set up quickly.

You also continually invest time and resources to maintain your servers.

When buying your own hardware, you have higher upfront costs.

Cloud solutions are cheaper to start with, but in the long-run, it can pay off to invest in your own infrastructure.

Pros and cons depend on your industry, so it is important to figure out your needs before making a decision.

Tarry Singh, CEO and Co-Founder at deepkapha.

ai, does not recommend to solely focus on the cloud for companies that develop algorithms as a competitive advantage.

Once you know how to leverage hardware for AI, consider the algorithmic part of AI next.

AlgorithmsAlgorithms are atop of AI’s holy trinity because they use data and infrastructure to churn out valuable products.

The algorithmic part of your AI Strategy is tricky.

Answering these questions will get you further.

Are proprietary algorithms a key driver of business value?Do you open-source your models or prefer to keep them proprietary?Cassie Kozyrkov, Chief Decision Scientist at Google, reckons that two worlds of Machine Learning exist.

Cassie likes to differentiate between Machine Learning Research and Applied Machine Learning.

Conducting research requires a different approach than applying existing algorithms.

The AI community has become much better at releasing public data sets and models that can be reused.

This provides a tremendous advantage to your company because you have access to the variety of the AI Model Zoo.

The main question you should answer in your AI Strategy is if algorithms are the main business driver for AI functions.

If yes, you should set up a patent program and incentivize employees to file for patents.

SkillsOnce the holy trinity of AI is in place, you need people to fulfill its destiny.

People are at the core of putting your data, infrastructure, and algorithms to work to generate business value.

How do you empower the people in your organization to use AI?.Answer the following questions in your AI Strategy:Do you build an in-house team or do you outsource tasks?How do you continually educate management and employees about AI?Andrew Ng recommends building an in-house AI team.

AI feeds off domain knowledge, and that can be hard to outsource in certain industries.

Outside consultants likely don’t know your data, infrastructure, and problems as well as your own employees.

Hence, the feasible way is to bundle enthusiastic employees and educate them about AI.

Photo by Tim Mossholder on UnsplashDominik Haitz, Data Scientist at 1&1 Ionos, states that AI as a novel technology differs from other tech innovations.

People are often not only unaware of AI’s actual capabilities, but they frequently have misconceptions about it.

This can range from ‘omnipotent threat to humanity’ to the notion of a multipurpose system that works straightforwardly out of the box.

Once the in-house team is in place, they need to act as an enabler.

The promises of AI are too vast to encapsulate them in a single team.

The AI Strategy should implement a program that continually educates all people to look for AI use-cases.

Very often, these programs should target high-impact individuals who can invest in AI projects.

Rachel Berryman, Co-Founder of todoku.

ai, is convinced that AI understanding of managers is crucial, as it will trickle down as AI opportunities to employees in their line.

Let’s investigate the final component of your AI Strategy — the organization.

OrganizationThe last but arguably most important component of the AI Strategy is to prepare your organization for AI.

Evaluate specifically your organizational design and the development processes.

Then, align them with best practices.

How do you enable your AI team to provide business value across teams and domains?Are your processes ready for the ML workflow?The benefits of Artificial Intelligence are omnipotent.

It is paramount to understand that AI cannot work in silos.

Instead of working in vertical customer-focused business units, AI can be seen as a horizontal enabler of the company.

To do that, Andrew Ng recommends establishing a separate unit which becomes the central enabling point of AI across the company.

This unit then works together with existing departments to find high-impact AI projects and to support their implementation.

SourceEnabling AI across the company requires understanding the Machine Learning Workflow.

Machine Learning follows a highly iterative process, with the outcome far from certain.

You can use tools like the AI Project Canvas to evaluate the potential for success, but you can hardly guarantee specific outcomes.

The very exploratory nature of AI makes it hard to follow company-wide goal measurements.

You can’t promise a working model without thoroughly evaluating the data.

Thus, it is difficult to estimate the concrete business impact of AI projects without first investing in ETL and initial data analysis.

— Rachel BerrymanConsider your processes: are they ready to support AI?.If you work in a safety-critical industry, chances are that processes don’t exist to verify statistical learning models.

Does your company follow waterfall engineering processes?.Reconsider your current development process and check if it aligns with the Machine Learning Workflow.

Now that you understand the core components of an AI Strategy, let’s look at more tips to avoid common pitfalls.

The Good, the Bad, and the Ugly AI StrategyWho do you need on your team to create an AI Strategy?.What constitutes a good or a bad AI Strategy?.How does an AI Strategy differ between corporations and startups?.This last abstract aims to answer the questions before.

Photo by Franki Chamaki on UnsplashThe AI Strategy TeamCreating an AI Strategy is a team effort.

You need diverse perspectives across the core components of an AI Strategy.

The team combination differs between startups and corporations.

Startups create an AI strategy in smaller teams centered around the technical feedback from the Data Engineer and the business feedback from the Product Owner or Business Developer.

Corporate teams involve more functionalities.

Andreas Meier, who created an internal AI Strategy for the world’s largest car manufacturer, knows that in corporations with specialized roles, you need a lot of domain knowledge to find a feasible AI Strategy.

You need a large group of people with different roles in a corporation, while in startups you can create a splendid AI Strategy with a few generalists.

Hallmarks of good and bad AI StrategiesAcross companies, good and bad AI Strategies share common traits.

Good AI Strategies are impact-driven, well-supported in the company, and well-funded in terms of time, salary, and expectations.

AI Strategy for corporations and startupsCreating an AI Strategy is different for corporations and startups.

Raphael Kohler explains that corporations have to consider legacy systems and are also challenged with change management of the existing organization, while startups can focus on entering the virtuous cycle of AI.

Andreas Meier knows that it can be overwhelming to lay out a path for AI to have an impact.

He states that in large corporations, plenty of potential exists to automate processes with AI.

To Andreas, it is important to simply start out and deliver value.

SourceOn the other side, startups should focus on delivering a product that works well without AI but steadily improves the more customers use the product.

Customer interactions are then analyzed to improve the product, thus luring ever more users.

Once they have entered the virtuous cycle of AI, AI startups are on a path to success.

Data is the oil that feeds the AI engine, and it can not be understated how important it is to think through how to acquire the initial set as well as the right information from customers to improve the product iteratively.

— Jason RischKey TakeawaysThe core components of an AI Strategy are woven into each other and interdependent.

The core components might be of different importance in different industries, but they are always relevant.

An AI Strategy should always serve the higher company strategyThe core components of an AI Strategy are Data, Infrastructure, Algorithms, Skills, and OrganizationThe AI Strategy teams should consist of Product Managers, Data Scientists, and Business DevelopersA good AI Strategy focus on mid-term goals and a holistic approach over hype-driven employment of a few Data ScientistsThe right timing of strategic data acquisition can make or break your AI fortunesIn this article, you learned how to approach creating an AI Strategy.

Think of AI’s core components when creating your AI Strategy.

We are looking forward to a world that embraces the decade of AI implementation.